Universal Features in Phonological Neighbor Networks

TL;DR

This study reveals that the topological features of phonological neighbor networks across multiple languages are primarily driven by string universals and form length distributions, not language-specific phonological properties.

Contribution

It demonstrates that universal network features are due to string universals and form length distributions, challenging the assumption that they reflect language-specific phonological structures.

Findings

PNNs share consistent topological features across languages.

Random lexicons with minimal linguistic realism produce similar PNN topologies.

Form length distribution significantly influences network topology.

Abstract

Human speech perception involves transforming a countinous acoustic signal into discrete linguistically meaningful units, such as phonemes, while simultaneously causing a listener to activate words that are similar to the spoken utterance and to each other. The Neighborhood Activation Model (NAM~\cite{Luce:1986,Luce:1998}) posits that phonological neighbors (two forms [words] that differ by one phoneme) compete significantly for recognition as a spoken word is heard. This definition of phonological similarity can be extended to an entire corpus of forms to produce a phonological neighbor network~\cite{Vitevitch:2008} (PNN). We study PNNs for five languages: English, Spanish, French, Dutch, and German. Consistent with previous work, we find that the PNNs share a consistent set of topological features. Using an approach that generates random lexicons with increasing levels of phonological realism, we show that even random forms with minimal relationship to any real language, combined with only the empirical distribution of language-specific phonological form lengths, are sufficient to produce the topological properties observed in the real language PNNs. The resulting pseudo-PNNs are insensitive to the level of lingusitic realism in the random lexicons but quite sensitive to the shape of the form length distribution. We therefore conclude that "universal" features seen across multiple languages are really string universals, not language universals, and arise primarily due to limitations in the kinds of networks generated by the one-step neighbor definition. Taken together, our results indicate that caution is warranted when linking the dynamics of human spoken word recognition to the topological properties of PNNs, and that the investigation of alternative similarity metrics for phonological forms should be a priority.